Abstract
Initially designed to infer evolutionary relationships based on morphological and physiological characters, phylogenetic reconstruction methods have greatly benefited from recent developments in molecular biology and sequencing technologies with a number of powerful methods having been developed specifically to infer phylogenies from macromolecular data. This chapter, while presenting an overview of basic concepts and methods used in phylogenetic reconstruction, is primarily intended as a simplified step-by-step guide to the construction of phylogenetic trees from nucleotide sequences using fairly up-to-date maximum likelihood methods implemented in freely available computer programs. While the analysis of chloroplast sequences from various Vanilla species is used as an illustrative example, the techniques covered here are relevant to the comparative analysis of homologous sequences datasets sampled from any group of organisms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Darlu P, Tassy P (1993) La reconstruction phylogénétique. Concepts et Méthodes. Masson
Groves C (1986) Systematics of the great apes. In: Swindler DR, Erwin J (eds) Comparative primate biology: systematics, evolution and anatomy, vol 1. Liss AR, New York, pp 187–217
Hemsley AR, Poole I (2004) The evolution of plant physiology. From whole plants to ecosystems. Elsevier Academic Press, Amsterdam
Caputo P (1997) DNA and phylogeny in plants: history and new perspectives. Lagascalia 19:331–344
Zuckerkandl E, Pauling L (1965) Molecules as documents of evolutionary history. J Theor Biol 8:357–366
Nei M, Kumar S (2000) Molecular evolution and phylogenetics. Oxford University Press, New York
Van de Peer Y (2009) Phylogeny inference based on distance methods. In: Salemmi M, Vandamme AM (eds) The phylogenetic handbook, a practical approach to DNA and protein phylogeny. Cambridge University Press, New York, pp 101–135
Michener CD, Sokal RR (1956) A quantitative approach to a problem in classification. Evolution 11:130–162
Fitch WM, Margoliash E (1967) Construction of phylogenetic trees. Science 155:279–284
Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425
Gascuel O (1997) BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Mol Biol Evol 14: 685–695
Steel MA, Hendy MD, Penny D (1988) Loss of information in genetic distances. Nature 336:118
Felsenstein J (2004) Inferring phylogenies. Sinauer Associates, Sunderland
Sober E (1988) Reconstructing the past: parsimony, evolution, and inference. MIT Press, Cambridge
Edwards AWF, Cavalli-Sforza LL (1964) Reconstruction of evolutionary trees. In: Heywood VH, McNeill J (eds) Phenetic and phylogenetic classification: a symposium. Systematics Association, London, pp 67–76
Cavalli-Sforza LL, Edwards AWF (1967) Phylogenetic analysis: models and estimation procedures. Evolution 32:550–570
Felsenstein J (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol 17:368–376
Farris JS (1970) Methods for computing Wagner trees. Syst Zool 19:83–92
Fitch WM (1971) Towards defining the course of evolution: minimum change for a specific tree topology. Syst Zool 20:406–416
Kluge AG, Farris JS (1969) Quantitative phyletics and the evolution of anurans. Syst Zool 18:1–32
Harrison CJ, Langdale JA (2006) A step by step guide to phylogeny reconstruction. Plant J 45:561–572
Aldrich J (1997) R. A. Fisher and the making of maximum likelihood 1912–1922. Statist Sci 12:162–176
Felsenstein J (1973) Maximum-likelihood estimation of evolutionary trees from continuous characters. Am J Hum Genet 25:471–492
Schmidt HA, von Haeseler A (2009) Phylogenetic inference using maximum likelihood methods. In: Salemmi M, Vandamme AM (eds) The phylogenetic handbook, a practical approach to DNA and protein phylogeny. Cambridge University Press, New York, pp 181–209
Hendy MD, Penny D (1982) Branch and bound algorithms to determine minimal evolutionary trees. Math Biosci 59:277–290
Swofford DL, Sullivan J (2003) Phylogeny inference based on parsimony and other methods using Paup*. In: Salemmi M, Vandamme AM (eds) The phylogenetic handbook, a practical approach to DNA and protein phylogeny. Cambridge University Press, New York, pp 267–312
Swofford DL, Olsen GJ (1990) Phylogeny reconstruction. In: Hillis DM, Moritz C, Mable BK (eds) Molecular systematics. Sinauer Associates, Sunderland, pp 411–501
Swofford DL et al (1996) Phylogenetic inference. In: Hillis DM, Moritz C, Mable BK (eds) Molecular systematics. Sinauer Associates, Sunderland, pp 407–514
Ronquist F, van der Mark P, Huelsenbeck JP (2009) Bayesian phylogenetic analysis using MrBayes. In: Salemmi M, Vandamme AM (eds) The phylogenetic handbook, a practical approach to DNA and protein phylogeny. Cambridge University Press, New York, pp 210–266
Tamura K et al (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739
Posada D (2008) jModelTest: phylogenetic model averaging. Mol Biol Evol 25: 1253–1256
Guindon S et al (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59:307–321
Morariu V et al (2008) Automatic online tuning for fast Gaussian summation. Advances in Neural Information Processing Systems (NIPS) 1–8
Hall BG (2007) Phylogenetic trees made easy: a how-to manual, 3rd edn. Sinauer Associates, Sunderland
Benson DA et al (1994) GenBank. Nucleic Acids Res 22:3441–3444
Cochrane G et al (2009) Petabyte-scale innovations at the European Nucleotide Archive. Nucleic Acids Res 37:D19–D25
Tateno Y et al (2002) DNA Data Bank of Japan (DDBJ) for genome scale research in life science. Nucleic Acids Res 30:27–30
Bouetard A et al (2010) Evidence of transoceanic dispersion of the genus Vanilla based on plastid DNA phylogenetic analysis. Mol Phyl Evol 55:621–630
Altschul SF et al (1990) Basic local alignment tool. J Mol Biol 215:403–410
Maddison WP, Donoghue MJ, Maddison DR (1984) Outgroup analysis and parsimony. Syst Zool 33:83–103
Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680
Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32: 1792–1797
Posada D, Crandall KA (1998) Model test: testing the model of substitution. Bioinformatics 14:817–818
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464
Minin V et al (2003) Performance-based selection of likelihood models for phylogeny estimation. Syst Biol 52:674–683
Luo A et al (2010) Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets. BMC Evol Biol 10:242
Ripplinger J, Sullivan J (2008) Does choice in model selection affect maximum likelihood analysis? Syst Biol 57:76–85
Jukes TH, Cantor CR (1969) Evolution of protein molecules. In: Munro HN (ed) Mammalian protein metabolism. Academic, New York, pp 21–132
Tavaré S (1986) Some probabilistic and statistical problems in the analysis of DNA sequences. Lect Math Life Sci (Am Math Soc) 17:57–86
Hasegawa M, Kishino H, Yano T (1985) Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol 22:160–174
Felsenstein J (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39:783–791
Anisimova M, Gascuel O (2006) Approximate likelihood-ratio test for branches: a fast, accurate, and powerful alternative. Syst Biol 55:539–552
Anisimova M et al (2011) Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood-based approximation schemes. Syst Biol 60:685–699
Darriba D et al (2011) ProtTest3: fast selection of best-fit models of protein evolution. Bioinformatics 27:1164–1165
Ruths D, Nakhleh L (2005) Recombination and phylogeny: effects and detection. Int J Bioinform Res Appl 1:202–212
Posada D, Crandall KA (2002) The effect of recombination on the accuracy of phylogeny estimation. J Mol Evol 54:396–402
Ronquist F, Huelsenbeck JP (2003) MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19:1572–1574
Drummond AJ et al (2012) Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol Biol Evol 29:1969–1973. doi:10.1093/molbev/mss075
Rannala B, Yang Z (1996) Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference. J Mol Evol 43:304–311
Mau B, Newton M, Larget B (1999) Bayesian phylogenetic inference via Markov chain Monte Carlo methods. Biometrics 55:1–12
Acknowledgments
ADB is supported by the Conseil Général de La Réunion and CIRAD. DPM is supported by the Wellcome Trust. PL is supported by CIRAD and Conseil Régional de La Réunion and European Union (FEDER). The authors wish to thank Dr. Jean-Michel Lett for his helpful comments.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer New York
About this protocol
Cite this protocol
De Bruyn, A., Martin, D.P., Lefeuvre, P. (2014). Phylogenetic Reconstruction Methods: An Overview. In: Besse, P. (eds) Molecular Plant Taxonomy. Methods in Molecular Biology, vol 1115. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-767-9_13
Download citation
DOI: https://doi.org/10.1007/978-1-62703-767-9_13
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-766-2
Online ISBN: 978-1-62703-767-9
eBook Packages: Springer Protocols